# Training-Time vs Inference-Time Evaluation

Evaluating during training to guide model development, versus evaluating live behaviour on real traffic.

**Rests on:** Offline vs Online Evaluation

*LLMs and Generative AI · LLM Evaluation*


## Why learn this

This concept is the first step in understanding how AI systems are tested and trusted. Without it, you cannot tell whether a model is learning properly or merely memorising, and you cannot know whether it will behave itself in the real world. If you skip this, every later evaluation concept will assume you already know the difference between the two phases and why they answer different questions. The goal here is to see how we ensure an AI is getting better while we build it, and how we make sure it doesn't get worse after we release it.


## The intuition

Imagine you are learning to ride a bicycle. Training-time evaluation is what happens in the empty parking lot while you practise: a friend watches you wobble, fall, and slowly get better, and tells you after each attempt whether you stayed upright. That feedback shapes how you ride next time. Inference-time evaluation is what happens later, on a busy city street, when you are actually delivering parcels: a supervisor checks whether you arrive on time, whether you follow traffic rules, and whether you crash. Both are 'evaluation', but they ask completely different questions. The first asks 'am I learning?', the second asks 'do I work under real conditions?'. In AI, training-time evaluation happens while the model is being built, using a dataset we control. It tells us whether the model is improving as we adjust it. Inference-time evaluation happens after the model is released, using real traffic from actual users. It tells us whether the model is still doing its job in the messy, unpredictable world. Neither can replace the other: you can practise perfectly in the parking lot and still fail on the street, and you cannot learn to ride by only being judged on the street. AI needs both, and this page is about when each is used, what each can and cannot tell you, and why you must never confuse the two.


## An analogy

Think of a chef learning to cook a signature dish. Training-time evaluation is the chef tasting the dish during the cooking process — in the quiet kitchen, with their own ingredients, before any customer arrives. They taste, adjust salt, taste again, and keep iterating. The feedback is immediate and private; if the dish is terrible, nobody but the chef and a few colleagues knows. The goal is purely to improve the recipe. Inference-time evaluation is the restaurant opening for dinner, with real customers ordering that dish. Now the chef cannot pause the kitchen to fine-tune the recipe; they must serve what they have. Feedback comes from customers: some praise, some send it back, some never return. This feedback is slow, public, and costly — a bad evening means lost revenue and a damaged reputation. The chef cannot ask the customer to wait while they fix the seasoning. Where the analogy stops working: in AI, training-time evaluation can be fully automated — the model is checked against a fixed dataset millions of times, automatically, and the adjustments are also automatic. A chef cannot taste a million dishes in an afternoon, but an AI can. Also, inference-time evaluation in AI is continuous: the restaurant never closes, so feedback streams in 24/7, and sometimes the model itself can be updated while it serves, which a chef cannot do mid-shift.


## Definition

Training-time evaluation is measuring how well a model is learning during development using a fixed dataset, while inference-time evaluation is measuring how well a deployed model behaves on live, real-world traffic continuously.


## How it connects

You are starting with no background, so this page is your entry point. It builds on only arithmetic and a few everyday ideas like 'learning' and 'measuring'. After this, you will be ready for the neighbouring topics in your library: Evaluation Datasets, because you need a fixed dataset to do training-time evaluation at all; Offline vs Online Evaluation, which extends this same distinction into the release workflow; and Regression Gates, which use evaluation results to decide whether to deploy. The lessons from Practical LLM Evaluation for Production Systems that you noted — 'evaluation is a decision system, not a score' and 'traditional ML evaluation breaks on four fronts' — both live inside this distinction: training-time evaluation is a decision system for the developer, inference-time is a decision system for the operator.


## The mathematics

This concept does not require algebra or calculus. It rests on only arithmetic and the idea of comparison. The branch of mathematics involved is descriptive statistics — counting, averaging, and comparing numbers. That is because evaluation is fundamentally about measuring: how often does the model get something right, and how does that compare to a previous version? You will not derive a formula here; you will learn what a rate and a difference are. The operation you will use most is the rate — a fraction that tells you the proportion of correct answers out of all attempts. For example, if a model answers 8 out of 10 questions correctly, the rate is 8 divided by 10, which is 0.8 or 80%. The other operation is the comparison of two numbers: is the new rate higher than the old rate? That is a subtraction. If the model was at 70% last week and is at 80% this week, the improvement is 10 percentage points. That is all the mathematics you need.

Training-time evaluation often uses the same rates, but on a fixed dataset; inference-time evaluation uses rates computed on live traffic, which may change every minute. The numbers mean the same thing; the context changes the interpretation.

accuracy = (number of correct predictions) / (total number of predictions) × 10%

No further symbols are needed for this concept. The formula is just a rate.


## A worked example

```
A chatbot is being developed to handle customer complaints. During training, the developer runs the model on a fixed set of 100 sample complaints and counts how many the model resolves correctly. At inference time, after deployment, the same chatbot faces real complaints from customers, and the team tracks how many of the first 50 real complaints are resolved correctly.

Step 1: Training-time evaluation, week 1: 60 out of 100 correct = 60/100 = 0.60 (60%)   # This is the rate of correct answers on the fixed training dataset during week 1.
Step 2: Training-time evaluation, week 2: 75 out of 100 correct = 75/100 = 0.75 (75%)   # After some adjustments to the model, the rate improved on the same dataset.
Step 3: Improvement = 0.75 - 0.60 = 0.15 (15 percentage points)   # This tells the developer the model is learning, because the fixed dataset is unchanged.
Step 4: Inference-time evaluation, first day: 35 out of 50 real complaints resolved correctly = 35/50 = 0.70 (70%)   # On real traffic, the model performs slightly worse than on the training set.
Step 5: Difference = 0.75 (training) - 0.70 (inference) = 0.05 (5 percentage points lower at inference)   # This gap shows the model does not generalise perfectly to real-world variability.

Result: The model improved during training (from 60% to 75%), but at inference time it achieved only 70% on real traffic, a 5-percentage-point drop. This illustrates that training-time evaluation does not guarantee inference-time performance.
```


## From scratch, in Python

```python
# Evaluating a model during training vs at inference
# We simulate a simple model that predicts 'resolved' or 'not resolved' for customer complaints.

# Step 1: We need a list of predictions. In Python, a list is an ordered collection of items.
# Here is a list of 5 predictions: 1 means 'resolved', 0 means 'not resolved'.
predictions = [1, 0, 1, 1, 0]

# The true outcomes for those same 5 cases (what actually happened).
true_outcomes = [1, 1, 0, 1, 0]

# To evaluate, we compare each prediction to its true outcome.
# We will count how many match. This is a loop: we go through each pair.
correct = 0
total = len(predictions)  # len() gives the number of items, here 5.

for i in range(total):  # range(total) gives 0,1,2,3,4
    if predictions[i] == true_outcomes[i]:  # == checks equality
        correct = correct + 1

# The rate is the number correct divided by the total.
rate = correct / total  # This gives a number between 0 and 1.
print(f"Training-time accuracy: {rate*100:.1f}%")  # .1f rounds to 1 decimal

# Now, for inference, we have a different set of predictions and outcomes — real traffic.
# The same code runs, but on different lists.
real_predictions = [1, 0, 1, 0, 1]
real_outcomes = [1, 1, 1, 0, 1]

correct_inference = 0
total_inference = len(real_predictions)

for i in range(total_inference):
    if real_predictions[i] == real_outcomes[i]:
        correct_inference = correct_inference + 1

rate_inference = correct_inference / total_inference
print(f"Inference-time accuracy: {rate_inference*100:.1f}%")

# The code is identical; the difference is the data feed into it.
```


## The same thing, with a framework

```python
In practice, you would not write this loop by hand. Libraries like scikit-learn provide a function called accuracy_score that does exactly this comparison. Here is how you would use it:

from sklearn.metrics import accuracy_score

training_predictions = [1, 0, 1, 1, 0]
training_true = [1, 1, 0, 1, 0]
real_predictions = [1, 0, 1, 0, 1]
real_true = [1, 1, 1, 0, 1]

print(f"Training-time accuracy: {accuracy_score(training_true, training_predictions)*100:.1f}%")
print(f"Inference-time accuracy: {accuracy_score(real_true, real_predictions)*100:.1f}%")

The accuracy_score function does the same thing as the loop above: it counts matches and divides by the total. The difference is that you call a library instead of writing the loop yourself.
```


## What the code does

The from_scratch code starts by defining two lists: predictions and true_outcomes. A list is a collection of items in order. We use 1 to mean 'resolved' and 0 to mean 'not resolved'. Then we count matches using a for loop: the loop goes through each index i from 0 to length-1, compares predictions[i] to true_outcomes[i], and adds 1 to correct if they are equal. After the loop, correct is the number of matches, and total is the length of the list. The rate is correct / total, a number between 0 and 1. We print it with a percentage sign. Then we repeat the same process for the real traffic lists. The key lesson is that the code is the same; only the data changes. The framework version uses sklearn.metrics.accuracy_score, which does the same comparison internally — the library call hides the loop but not the arithmetic.


## Common misconceptions

**Training-time and inference-time are the same evaluation just at different times** — A metric that works for one automatically works for the other.

Training-time evaluation uses a fixed dataset and has a known correct answer. Inference-time evaluation faces real traffic, where there is no ground truth and the distribution changes. In fact: They need different metrics and different infrastructure. Training-time can use accuracy against labels; inference-time often needs proxies like user feedback or safety checks.

**Good training-time accuracy means good runtime behaviour** — If the model scores 95% on the training set, it will work well in production.

The training set is a curated sample; real traffic includes edge cases, new language, and adversarial inputs. In fact: Always monitor inference-time performance separately; drift between the two is expected and must be tracked.

**Inference-time evaluation catches everything** — Once the model is live, the runtime metrics will tell us if it is bad.

Real traffic feedback is slow and rare — a model can be subtly wrong for a long time before anyone notices. In fact: Training-time evaluation is faster and cheaper for catching systematic errors before release.

**Evaluation is just a score** — A single number like accuracy is enough to decide if the model is good or not.

As your library notes, evaluation is a decision system — metrics that do not gate a decision are decoration. In fact: Each evaluation should be tied to a decision: whether to keep training, whether to deploy, whether to roll back.

**You can re-use the training set for inference-time evaluation** — The same questions can be used to check live performance.

The training set is fixed and becomes stale; real traffic changes over time. In fact: Use a dedicated gold set for training-time, and sample real traffic for inference-time. They must be kept separate.


## In short

Training-time evaluation measures learning progress on a fixed dataset; inference-time evaluation measures live behaviour on real traffic. They answer different questions, and neither replaces the other. A model can excel at training time and fail at runtime, or vice versa. Always monitor both, with separate data and metrics, and treat each as a decision gate.


## Where this is used

This concept is fundamental to how every AI system is built and monitored. In large language model development, training-time evaluation happens on fixed benchmarks like MMLU or on custom gold sets. The team runs the model against these datasets after each training run, sees the accuracy, and decides whether to adjust the training hyperparameters. Inference-time evaluation happens when the model is deployed: the system logs every user input and the model's response, and a dashboard shows metrics like response quality, safety, and cost in real time. A developer might write code to sample 1% of live traffic, run a model judge on the sampled responses, and compute a quality score daily. In small language models running on a phone, training-time evaluation happens before release on a test set designed to mimic on-device conditions; inference-time evaluation often cannot be done continuously because the device is offline, so the developer relies on occasional telemetry: when the phone connects to the internet, it sends anonymised logs of the model's outputs, and the team evaluates those. In retrieval-augmented generation, training-time evaluation checks whether the retriever finds the right documents for a set of test questions, and whether the generator's answer is grounded in those documents. Inference-time evaluation adds a continuous check: does the system return an answer that is not in the retrieved context? That is a failure a developer would monitor live. In agentic systems that plan and call tools, training-time evaluation is done with simulated environments: the agent is given a task, allowed to call a mock tool, and the evaluator checks whether it took the right sequence of actions. Inference-time evaluation is harder because the agent acts in the real world: a tool call might fail, the environment might change, and the evaluator must decide in real time whether the agent's final output is acceptable. In everyday applied machine learning — recommendation, vision, fraud detection — training-time evaluation uses a held-out test set to measure precision and recall, and inference-time evaluation monitors the live metrics: the fraud detection model's false-positive rate is tracked hourly because that directly affects customer experience. The developer's practical work is to keep the two evaluations separate: the training set must not leak into runtime; the runtime metrics must trigger alerts when they drift below a threshold. A common mistake is to evaluate at training time with a dataset that does not resemble real traffic, giving a false sense of confidence. The alternative mistake is to evaluate at inference time without a stable reference point, making it impossible to tell whether a change in quality is due to the model or to the traffic.


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Read this on the web: https://basavapurushottam.com/learn-ai/training-time-vs-inference-time-evaluation
